What we’re about
WiMLDS's mission is to support and promote women and gender minorities who are practicing, studying or are interested in the fields of machine learning and data science. We create opportunities for members to engage in technical and professional conversations in a positive, supportive environment by hosting talks by women and gender minority individuals working in data science or machine learning, technical workshops, networking events and hackathons. We are inclusive to anyone who supports our cause regardless of gender identity or technical background. However, in support of our mission, priority for certain events and opportunities will be given to women and gender minority members.
Our Code of Conduct ( https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct ) is available online and applies to all our spaces, both online and off.
• Follow @wimlds ( https://twitter.com/wimlds ) on Twitter for general WiMLDS news or visit http://wimlds.org ( http://wimlds.org/ ) to learn about our chapters in other cities.
• Women & gender minorities are invited to join the global WiMLDS Slack group by sending an email to slack@wimlds.org.
Upcoming events (1)
See all- All about RAGs - How to develop RAG based solutions, Origin, Evolution, Use CaseClearRoute, Pune
Stimulating Talk, Live Demos and Interactive discussions by Founder Mohammed Lokhandwala : Investor TrustTalk, Mechatron, Chemistcraft ++
All About RAGs: Foundations, Evolution, and Hands-On Application Development
## 👥 Target Audience:
- Beginners to GenAI
- Developers new to RAG-based architecture
- Product/solution architects exploring GenAI integration
- AI/ML enthusiasts interested in applied use cases
***
## 🎯 Objectives:
- Understand what RAG is and why it matters
- Trace the origin and evolution of RAG in the context of GenAI
- Learn how to build RAG systems step-by-step
- Explore unique, real-world use cases that RAG uniquely solves
- Position RAG in today’s GenAI ecosystem alongside Agents and other paradigms
***
## 🧱 Session Breakdown & Topics:
### 🟦 1. Setting the Context: Why RAG?
- The limitations of LLMs (hallucination, knowledge cutoff)
- Need for real-time, factual grounding
- Transition from static to retrieval-augmented knowledge flows
- Intro to retrieval-augmented generation (RAG)
### 🟦 2. Origins & Evolution of RAG
- Early use of embeddings and vector search (pre-LLMs)
- Evolution of toolchains: Pinecone, Weaviate, LangChain, LlamaIndex
### 🟦 3. Core Components of a RAG Architecture
- Embedding Models (OpenAI, Cohere, SentenceTransformers)
- Vector Databases (Chroma, Pinecone, Weaviate, FAISS)
- Chunking, indexing, retrieval
- Prompt engineering & context injection
- Generation via LLM (OpenAI, Anthropic, Mistral, etc.)
- Evaluation techniques (retrieval precision, groundedness)
### 🟦 4. Building Your First RAG Application: Hands-On Guide
- 🔹 Use case selection (e.g., internal knowledge assistant, customer FAQ bot)
- 🔹 Data preparation: chunking, metadata, format
- 🔹 Creating embeddings (with open-source or API)
- 🔹 Choosing and using a vector DB
- 🔹 Implementing retrieval and generation logic
- 🔹 UI + deployment (e.g., Streamlit, Gradio, LangChain + FastAPI)
- 🔹 Optional: Add memory, conversation history, feedback loops
### 🟦 5. Advanced Topics (Briefly Introduce)
- Hybrid Retrieval (semantic + keyword)
- Structured RAG (e.g., SQL-based retrieval)
- Agent-RAG hybrid systems
### 🟦 6. Use Cases that Only RAG Can Solve Well
- 🔸 Legal document assistants grounded in latest court rulings
- 🔸 Internal enterprise Q&A systems (doc + intranet)
- 🔸 Academic research summarizers across journals
- 🔸 Real-time support chatbots referencing dynamic product manuals
- 🔸 Domain-specific compliance tools
### 🟦 7. RAG vs. Agents vs. Fine-tuning
- When to use RAG vs fine-tuning
- When to layer Agents on top of RAG
- RAG is not obsolete: it's foundational for grounding and precision
- RAG’s role in AI assistants (retrieval for tools, memory, real-time data)
- We can also add "Conceptual introduction to Agentic and tools"
### 🟦 8. Best Practices and Pitfalls
- Chunking strategy & context window size
- Evaluation metrics (factuality, latency, hallucination rate)
- Common mistakes (irrelevant retrieval, poor chunking, bad metadata)
- Cost-performance tradeoffs
***
## 🔧 Suggested Tools & Stack for Demos:
- LangChain or LlamaIndex
- OpenAI / Cohere / HuggingFace embeddings
- ChromaDB / FAISS / Pinecone
- GitHub starter kits (can be pre-shared)
